List of Tables -- List of Figures -- Table of Notations -- Preface -- Road Map -- I. Preliminaries -- 1. Introduction -- 2. Mathematical Foundations -- 3. Linguistics Essentials -- 4. Corpus-Based Work -- II. Words -- 5. Collocations -- 6. Statistical Inference: n-gram Models over Sparse Data -- 7. Word Sense Disambiguation -- 8. Lexical Acquisition -- III. Grammar -- 9. Markov Models -- 10. Part-of-Speech Tagging -- 11. Probabilistic Context Free Grammars -- 12. Probabilistic Parsing -- IV. Applications and Techniques -- 13. Statistical Alignment and Machine Translation -- 14. Clustering -- 15. Topics in Information Retrieval -- 16. Text Categorization -- Tiny Statistical Tables -- Bibliography -- Index.

Statistical natural-language processing is, in my estimation, one of the most fast-moving and exciting areas of computer science these days. Anyone who wants to learn this field would be well advised to get this book. For that matter, the same goes for anyone who is already in the field. I know that it is going to be one of the most well-thumbed books on my bookshelf.\\\ -- Eugene Charniak, Department of Computer Science, Brown UniversityStatistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.